Matrix Factorization Collaborative-Based Recommender System for Riyadh Restaurants: Leveraging Machine Learning to Enhance Consumer Choice

Abstract

Saudi Arabia’s tourism sector has recently started to play a significant role as an economic driver. The restaurant industry in Riyadh has experienced rapid growth in recent years, making it increasingly challenging for customers to choose from the large number of restaurants available. This paper proposes a matrix factorization collaborative-based recommender system for Riyadh city restaurants. The system leverages user reviews and ratings to predict users’ preferences and recommend restaurants likely to be of interest to them. The system incorporates three different approaches, namely, non-negative matrix factorization (NMF), singular value decomposition (SVD), and optimized singular value decomposition (SVD++). To the best of our knowledge, this is the first recommender system specifically designed for Riyadh restaurants. A comprehensive dataset of restaurants in Riyadh was collected, scraped from Foursquare.com, which includes a wide range of restaurant features and attributes. The dataset is publicly available, enabling other researchers to replicate the experiments and build upon the work. The performance of the system was evaluated using a real-world dataset, and its effectiveness was demonstrated by comparing it to a state-of-the-art recommender system. The evaluation results showed that SVD and NMF are effective methods for generating recommendations, with SVD performing slightly better in terms of RMSE and NMF performing slightly better in terms of MAE. Overall, the findings suggest that the collaborative-based approach using matrix factorization algorithms is an effective way to capture the complex relationships between users and restaurants.